Advanced Multi-Objective Optimization Algorithms

This paper introduces EvoMOBO, a multi-objective Bayesian optimization framework for protein engineering, showing improved performance in enzyme tasks compared to wild-type variants.

This paper introduces a hybrid calibration framework that integrates expert knowledge into multi-objective optimization for biological model parameter estimation, showing improved biological relevance and reduced overfitting.

The paper introduces a multi-objective Bayesian optimization method that adaptively updates scalarization functions for biomedical data analysis workflows, demonstrating it on single-cell RNA sequencing and imaging data.

This study compares multi-objective evolutionary algorithms for modular cell design in biocatalysis, finding that real-world performance differs from artificial benchmarks and identifying effective parameter configurations.